The present invention relates to dynamic quantization, and more specifically, to a dynamic quantization method for the input of a soft decision decoder.
The purpose of a communication system is to transmit signals carrying information through a communication channel separating the transmitter from the receiver. FIG. 1 is a block diagram illustrating a conventional receiver of a digital communication system. The signals must be modulated to shift the original frequency range into other frequency ranges suitable for transmission, thus the receiving end must comprise a demodulator which reverses the modulation process. As shown in FIG. 1, the signal received by an antenna 11 is first passed to a demodulator 12. A Soft Output Viterbi Algorithm (SOVA) equalizer 13 receives the demodulated signal from the demodulator 12 and performs signal equalization according to the output of a channel estimator 14. An equalizer is generally required in the receiver to combat the inter symbol interference (ISI) induced when the channel bandwidth is close to the signal bandwidth as the ISI is caused by multi-path within time dispersive channel. According to the possible output values, the equalizer is partitioned into two categories, hard decision equalizer and soft decision equalizer. The output values of the hard decision equalizer are limited to only two possible values, “+1” and “−1”. For the soft decision equalizer, however, a variety of output values may be obtained. When the hard decision equalizer is employed, significant distortion is inevitable because of the simple implementation. As shown in FIG. 1, the equalizer 13 may enhance its performance by employing the soft output Viterbi algorithm (SOVA). The output values of the SOVA equalizer 13 are however not ranged, as a result, it is difficult to represent the output values with a limited set of bits.
A quantizer 15 is thus utilized to limit the values of the SOVA output signals by assigning them into a finite set of possible values. Quantization is the process of mapping a continuous range of amplitudes of a signal into a finite set of discrete values. Although the increase of quantization levels may improve the performance, extra computation burden is placed on the decoder.
The quantizer 15 determines the number of quantization levels regardless of channel conditions. Redundant bits may be used to represent the equalized outputs when the output signals of the SOVA equalizer 13 are highly reliable. It turns out that the soft Viterbi decoder 16 may suffer from unnecessary computational burden, and moreover, more power consumption is expected.
The output of the quantizer 15 is fed to a soft Viterbi Decoder 16 for data recovery. Similar to the equalizer, decoders can be categorized as hard decision decoders or soft decision decoders. The Viterbi algorithm performs efficient Maximum Likelihood Sequence Estimation (MLSE), a popular algorithm applicable to both soft decision equalizer and decoders. By implementing the Soft output Viterbi algorithm (SOVA) that accepts and delivers soft sample values, the signal to noise ratio (SNR) can be significantly improved. Generally, soft decision decoding is superior to hard decision decoding by approximately 2˜3 dB, but a soft decision decoder is more complicated as it performs additional computations to handle the soft sample values.